Overview

Dataset statistics

Number of variables24
Number of observations24000
Missing cells0
Missing cells (%)0.0%
Duplicate rows22
Duplicate rows (%)0.1%
Total size in memory5.1 MiB
Average record size in memory222.0 B

Variable types

Numeric20
Categorical4

Alerts

Dataset has 22 (0.1%) duplicate rowsDuplicates
RETRASO_PAGO_ESTADO_4 is highly overall correlated with RETRASO_PAGO_ESTADO_5 and 8 other fieldsHigh correlation
RETRASO_PAGO_ESTADO_5 is highly overall correlated with RETRASO_PAGO_ESTADO_4 and 9 other fieldsHigh correlation
RETRASO_PAGO_ESTADO_6 is highly overall correlated with RETRASO_PAGO_ESTADO_4 and 10 other fieldsHigh correlation
RETRASO_PAGO_ESTADO_7 is highly overall correlated with RETRASO_PAGO_ESTADO_4 and 9 other fieldsHigh correlation
RETRASO_PAGO_ESTADO_8 is highly overall correlated with RETRASO_PAGO_ESTADO_4 and 7 other fieldsHigh correlation
RETRASO_PAGO_ESTADO_9 is highly overall correlated with RETRASO_PAGO_ESTADO_6 and 2 other fieldsHigh correlation
DEUDA_MES_4 is highly overall correlated with RETRASO_PAGO_ESTADO_4 and 11 other fieldsHigh correlation
DEUDA_MES_5 is highly overall correlated with RETRASO_PAGO_ESTADO_4 and 13 other fieldsHigh correlation
DEUDA_MES_6 is highly overall correlated with RETRASO_PAGO_ESTADO_4 and 13 other fieldsHigh correlation
DEUDA_MES_7 is highly overall correlated with RETRASO_PAGO_ESTADO_4 and 12 other fieldsHigh correlation
DEUDA_MES_8 is highly overall correlated with RETRASO_PAGO_ESTADO_4 and 11 other fieldsHigh correlation
DEUDA_MES_9 is highly overall correlated with RETRASO_PAGO_ESTADO_5 and 9 other fieldsHigh correlation
PAGO_MES_4 is highly overall correlated with DEUDA_MES_4 and 4 other fieldsHigh correlation
PAGO_MES_5 is highly overall correlated with DEUDA_MES_4 and 6 other fieldsHigh correlation
PAGO_MES_6 is highly overall correlated with DEUDA_MES_4 and 6 other fieldsHigh correlation
PAGO_MES_7 is highly overall correlated with DEUDA_MES_4 and 8 other fieldsHigh correlation
PAGO_MES_8 is highly overall correlated with DEUDA_MES_5 and 7 other fieldsHigh correlation
PAGO_MES_9 is highly overall correlated with DEUDA_MES_6 and 5 other fieldsHigh correlation
PAGO_MES_8 is highly skewed (γ1 = 30.50317256)Skewed
RETRASO_PAGO_ESTADO_4 has 13031 (54.3%) zerosZeros
RETRASO_PAGO_ESTADO_5 has 13550 (56.5%) zerosZeros
RETRASO_PAGO_ESTADO_6 has 13171 (54.9%) zerosZeros
RETRASO_PAGO_ESTADO_7 has 12647 (52.7%) zerosZeros
RETRASO_PAGO_ESTADO_8 has 12607 (52.5%) zerosZeros
RETRASO_PAGO_ESTADO_9 has 11810 (49.2%) zerosZeros
DEUDA_MES_4 has 3264 (13.6%) zerosZeros
DEUDA_MES_5 has 2844 (11.8%) zerosZeros
DEUDA_MES_6 has 2556 (10.7%) zerosZeros
DEUDA_MES_7 has 2287 (9.5%) zerosZeros
DEUDA_MES_8 has 2035 (8.5%) zerosZeros
DEUDA_MES_9 has 1638 (6.8%) zerosZeros
PAGO_MES_4 has 5786 (24.1%) zerosZeros
PAGO_MES_5 has 5384 (22.4%) zerosZeros
PAGO_MES_6 has 5182 (21.6%) zerosZeros
PAGO_MES_7 has 4801 (20.0%) zerosZeros
PAGO_MES_8 has 4284 (17.8%) zerosZeros
PAGO_MES_9 has 4225 (17.6%) zerosZeros

Reproduction

Analysis started2023-04-01 06:16:13.587874
Analysis finished2023-04-01 06:17:01.768997
Duration48.18 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Cupo_TC
Real number (ℝ)

Distinct79
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167828.74
Minimum10000
Maximum800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size891.0 KiB
2023-04-01T01:17:01.876965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum800000
Range790000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129957.24
Coefficient of variation (CV)0.7743444
Kurtosis0.48624776
Mean167828.74
Median Absolute Deviation (MAD)90000
Skewness0.98527094
Sum4.0278897 × 109
Variance1.6888885 × 1010
MonotonicityNot monotonic
2023-04-01T01:17:02.014096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 2659
 
11.1%
20000 1583
 
6.6%
30000 1292
 
5.4%
80000 1257
 
5.2%
200000 1214
 
5.1%
150000 889
 
3.7%
100000 814
 
3.4%
180000 791
 
3.3%
360000 700
 
2.9%
60000 661
 
2.8%
Other values (69) 12140
50.6%
ValueCountFrequency (%)
10000 392
 
1.6%
16000 2
 
< 0.1%
20000 1583
6.6%
30000 1292
5.4%
40000 189
 
0.8%
50000 2659
11.1%
60000 661
 
2.8%
70000 597
 
2.5%
80000 1257
5.2%
90000 526
 
2.2%
ValueCountFrequency (%)
800000 2
 
< 0.1%
780000 2
 
< 0.1%
760000 1
 
< 0.1%
750000 3
< 0.1%
730000 1
 
< 0.1%
720000 3
< 0.1%
710000 5
< 0.1%
700000 7
< 0.1%
690000 1
 
< 0.1%
680000 3
< 0.1%

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size891.0 KiB
Femenino
14507 
Masculino
9493 

Length

Max length9
Median length8
Mean length8.3955417
Min length8

Characters and Unicode

Total characters201493
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemenino
2nd rowFemenino
3rd rowFemenino
4th rowFemenino
5th rowFemenino

Common Values

ValueCountFrequency (%)
Femenino 14507
60.4%
Masculino 9493
39.6%

Length

2023-04-01T01:17:02.150718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T01:17:02.266716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
femenino 14507
60.4%
masculino 9493
39.6%

Most occurring characters

ValueCountFrequency (%)
n 38507
19.1%
e 29014
14.4%
i 24000
11.9%
o 24000
11.9%
F 14507
 
7.2%
m 14507
 
7.2%
M 9493
 
4.7%
a 9493
 
4.7%
s 9493
 
4.7%
c 9493
 
4.7%
Other values (2) 18986
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 177493
88.1%
Uppercase Letter 24000
 
11.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 38507
21.7%
e 29014
16.3%
i 24000
13.5%
o 24000
13.5%
m 14507
 
8.2%
a 9493
 
5.3%
s 9493
 
5.3%
c 9493
 
5.3%
u 9493
 
5.3%
l 9493
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
F 14507
60.4%
M 9493
39.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 201493
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 38507
19.1%
e 29014
14.4%
i 24000
11.9%
o 24000
11.9%
F 14507
 
7.2%
m 14507
 
7.2%
M 9493
 
4.7%
a 9493
 
4.7%
s 9493
 
4.7%
c 9493
 
4.7%
Other values (2) 18986
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 38507
19.1%
e 29014
14.4%
i 24000
11.9%
o 24000
11.9%
F 14507
 
7.2%
m 14507
 
7.2%
M 9493
 
4.7%
a 9493
 
4.7%
s 9493
 
4.7%
c 9493
 
4.7%
Other values (2) 18986
9.4%

EDUCATION
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size891.0 KiB
universidad
11151 
Posgrado
8493 
colegio
3967 
5
 
230
otro
 
107
Other values (2)
 
52

Length

Max length11
Median length8
Mean length9.1285
Min length1

Characters and Unicode

Total characters219084
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuniversidad
2nd rowcolegio
3rd rowuniversidad
4th rowcolegio
5th rowPosgrado

Common Values

ValueCountFrequency (%)
universidad 11151
46.5%
Posgrado 8493
35.4%
colegio 3967
 
16.5%
5 230
 
1.0%
otro 107
 
0.4%
6 41
 
0.2%
0 11
 
< 0.1%

Length

2023-04-01T01:17:02.374715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T01:17:02.491731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
universidad 11151
46.5%
posgrado 8493
35.4%
colegio 3967
 
16.5%
5 230
 
1.0%
otro 107
 
0.4%
6 41
 
0.2%
0 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
d 30795
14.1%
i 26269
12.0%
o 25134
11.5%
r 19751
9.0%
s 19644
9.0%
a 19644
9.0%
e 15118
6.9%
g 12460
5.7%
u 11151
 
5.1%
v 11151
 
5.1%
Other values (8) 27967
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 210309
96.0%
Uppercase Letter 8493
 
3.9%
Decimal Number 282
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 30795
14.6%
i 26269
12.5%
o 25134
12.0%
r 19751
9.4%
s 19644
9.3%
a 19644
9.3%
e 15118
7.2%
g 12460
5.9%
u 11151
 
5.3%
v 11151
 
5.3%
Other values (4) 19192
9.1%
Decimal Number
ValueCountFrequency (%)
5 230
81.6%
6 41
 
14.5%
0 11
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
P 8493
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 218802
99.9%
Common 282
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 30795
14.1%
i 26269
12.0%
o 25134
11.5%
r 19751
9.0%
s 19644
9.0%
a 19644
9.0%
e 15118
6.9%
g 12460
5.7%
u 11151
 
5.1%
v 11151
 
5.1%
Other values (5) 27685
12.7%
Common
ValueCountFrequency (%)
5 230
81.6%
6 41
 
14.5%
0 11
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 30795
14.1%
i 26269
12.0%
o 25134
11.5%
r 19751
9.0%
s 19644
9.0%
a 19644
9.0%
e 15118
6.9%
g 12460
5.7%
u 11151
 
5.1%
v 11151
 
5.1%
Other values (8) 27967
12.8%

MARRIAGE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size891.0 KiB
soltero_a
12752 
Casado
10954 
otro
 
245
0
 
49

Length

Max length9
Median length9
Mean length7.563375
Min length1

Characters and Unicode

Total characters181521
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCasado
2nd rowsoltero_a
3rd rowCasado
4th rowCasado
5th rowCasado

Common Values

ValueCountFrequency (%)
soltero_a 12752
53.1%
Casado 10954
45.6%
otro 245
 
1.0%
0 49
 
0.2%

Length

2023-04-01T01:17:02.591733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T01:17:02.699732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
soltero_a 12752
53.1%
casado 10954
45.6%
otro 245
 
1.0%
0 49
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 36948
20.4%
a 34660
19.1%
s 23706
13.1%
t 12997
 
7.2%
r 12997
 
7.2%
l 12752
 
7.0%
e 12752
 
7.0%
_ 12752
 
7.0%
C 10954
 
6.0%
d 10954
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 157766
86.9%
Connector Punctuation 12752
 
7.0%
Uppercase Letter 10954
 
6.0%
Decimal Number 49
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 36948
23.4%
a 34660
22.0%
s 23706
15.0%
t 12997
 
8.2%
r 12997
 
8.2%
l 12752
 
8.1%
e 12752
 
8.1%
d 10954
 
6.9%
Connector Punctuation
ValueCountFrequency (%)
_ 12752
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 10954
100.0%
Decimal Number
ValueCountFrequency (%)
0 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 168720
92.9%
Common 12801
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 36948
21.9%
a 34660
20.5%
s 23706
14.1%
t 12997
 
7.7%
r 12997
 
7.7%
l 12752
 
7.6%
e 12752
 
7.6%
C 10954
 
6.5%
d 10954
 
6.5%
Common
ValueCountFrequency (%)
_ 12752
99.6%
0 49
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 181521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 36948
20.4%
a 34660
19.1%
s 23706
13.1%
t 12997
 
7.2%
r 12997
 
7.2%
l 12752
 
7.0%
e 12752
 
7.0%
_ 12752
 
7.0%
C 10954
 
6.0%
d 10954
 
6.0%

AGE
Real number (ℝ)

Distinct55
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.477542
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size891.0 KiB
2023-04-01T01:17:02.807736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2100143
Coefficient of variation (CV)0.25960125
Kurtosis0.035159016
Mean35.477542
Median Absolute Deviation (MAD)6
Skewness0.7296463
Sum851461
Variance84.824363
MonotonicityNot monotonic
2023-04-01T01:17:03.044068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1288
 
5.4%
27 1197
 
5.0%
28 1150
 
4.8%
30 1097
 
4.6%
26 988
 
4.1%
31 969
 
4.0%
25 951
 
4.0%
34 935
 
3.9%
32 920
 
3.8%
33 917
 
3.8%
Other values (45) 13588
56.6%
ValueCountFrequency (%)
21 54
 
0.2%
22 443
 
1.8%
23 755
3.1%
24 896
3.7%
25 951
4.0%
26 988
4.1%
27 1197
5.0%
28 1150
4.8%
29 1288
5.4%
30 1097
4.6%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 2
 
< 0.1%
73 4
 
< 0.1%
72 3
 
< 0.1%
71 3
 
< 0.1%
70 8
 
< 0.1%
69 11
< 0.1%
68 4
 
< 0.1%
67 12
0.1%
66 20
0.1%

RETRASO_PAGO_ESTADO_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.294375
Minimum-2
Maximum8
Zeros13031
Zeros (%)54.3%
Negative8526
Negative (%)35.5%
Memory size891.0 KiB
2023-04-01T01:17:03.176740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1537723
Coefficient of variation (CV)-3.9193963
Kurtosis3.5576163
Mean-0.294375
Median Absolute Deviation (MAD)0
Skewness0.9719961
Sum-7065
Variance1.3311905
MonotonicityNot monotonic
2023-04-01T01:17:03.292563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 13031
54.3%
-1 4566
 
19.0%
-2 3960
 
16.5%
2 2185
 
9.1%
3 149
 
0.6%
4 41
 
0.2%
7 39
 
0.2%
6 19
 
0.1%
5 9
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
-2 3960
 
16.5%
-1 4566
 
19.0%
0 13031
54.3%
2 2185
 
9.1%
3 149
 
0.6%
4 41
 
0.2%
5 9
 
< 0.1%
6 19
 
0.1%
7 39
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 39
 
0.2%
6 19
 
0.1%
5 9
 
< 0.1%
4 41
 
0.2%
3 149
 
0.6%
2 2185
 
9.1%
0 13031
54.3%
-1 4566
 
19.0%
-2 3960
 
16.5%

RETRASO_PAGO_ESTADO_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.26904167
Minimum-2
Maximum7
Zeros13550
Zeros (%)56.5%
Negative8095
Negative (%)33.7%
Memory size891.0 KiB
2023-04-01T01:17:03.396762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1371216
Coefficient of variation (CV)-4.226563
Kurtosis4.1787636
Mean-0.26904167
Median Absolute Deviation (MAD)0
Skewness1.0417451
Sum-6457
Variance1.2930455
MonotonicityNot monotonic
2023-04-01T01:17:03.485076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 13550
56.5%
-1 4430
 
18.5%
-2 3665
 
15.3%
2 2077
 
8.7%
3 138
 
0.6%
4 71
 
0.3%
7 51
 
0.2%
5 14
 
0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
-2 3665
 
15.3%
-1 4430
 
18.5%
0 13550
56.5%
2 2077
 
8.7%
3 138
 
0.6%
4 71
 
0.3%
5 14
 
0.1%
6 4
 
< 0.1%
7 51
 
0.2%
ValueCountFrequency (%)
7 51
 
0.2%
6 4
 
< 0.1%
5 14
 
0.1%
4 71
 
0.3%
3 138
 
0.6%
2 2077
 
8.7%
0 13550
56.5%
-1 4430
 
18.5%
-2 3665
 
15.3%

RETRASO_PAGO_ESTADO_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.22325
Minimum-2
Maximum8
Zeros13171
Zeros (%)54.9%
Negative8054
Negative (%)33.6%
Memory size891.0 KiB
2023-04-01T01:17:03.565766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1726964
Coefficient of variation (CV)-5.2528392
Kurtosis3.7079326
Mean-0.22325
Median Absolute Deviation (MAD)0
Skewness1.0402353
Sum-5358
Variance1.3752167
MonotonicityNot monotonic
2023-04-01T01:17:03.856064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 13171
54.9%
-1 4565
 
19.0%
-2 3489
 
14.5%
2 2480
 
10.3%
3 147
 
0.6%
4 62
 
0.3%
7 51
 
0.2%
5 28
 
0.1%
6 5
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
-2 3489
 
14.5%
-1 4565
 
19.0%
0 13171
54.9%
1 1
 
< 0.1%
2 2480
 
10.3%
3 147
 
0.6%
4 62
 
0.3%
5 28
 
0.1%
6 5
 
< 0.1%
7 51
 
0.2%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 51
 
0.2%
6 5
 
< 0.1%
5 28
 
0.1%
4 62
 
0.3%
3 147
 
0.6%
2 2480
 
10.3%
1 1
 
< 0.1%
0 13171
54.9%
-1 4565
 
19.0%

RETRASO_PAGO_ESTADO_7
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.17333333
Minimum-2
Maximum8
Zeros12647
Zeros (%)52.7%
Negative8052
Negative (%)33.6%
Memory size891.0 KiB
2023-04-01T01:17:03.937189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1956233
Coefficient of variation (CV)-6.8978268
Kurtosis2.3109799
Mean-0.17333333
Median Absolute Deviation (MAD)0
Skewness0.87667459
Sum-4160
Variance1.4295151
MonotonicityNot monotonic
2023-04-01T01:17:04.017208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 12647
52.7%
-1 4767
 
19.9%
-2 3285
 
13.7%
2 2992
 
12.5%
3 178
 
0.7%
4 65
 
0.3%
7 25
 
0.1%
6 21
 
0.1%
5 16
 
0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
-2 3285
 
13.7%
-1 4767
 
19.9%
0 12647
52.7%
1 2
 
< 0.1%
2 2992
 
12.5%
3 178
 
0.7%
4 65
 
0.3%
5 16
 
0.1%
6 21
 
0.1%
7 25
 
0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 25
 
0.1%
6 21
 
0.1%
5 16
 
0.1%
4 65
 
0.3%
3 178
 
0.7%
2 2992
 
12.5%
1 2
 
< 0.1%
0 12647
52.7%
-1 4767
 
19.9%

RETRASO_PAGO_ESTADO_8
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.14245833
Minimum-2
Maximum8
Zeros12607
Zeros (%)52.5%
Negative7896
Negative (%)32.9%
Memory size891.0 KiB
2023-04-01T01:17:04.101205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.194525
Coefficient of variation (CV)-8.3850837
Kurtosis1.6738447
Mean-0.14245833
Median Absolute Deviation (MAD)0
Skewness0.79652644
Sum-3419
Variance1.4268901
MonotonicityNot monotonic
2023-04-01T01:17:04.181206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 12607
52.5%
-1 4818
 
20.1%
2 3109
 
13.0%
-2 3078
 
12.8%
3 244
 
1.0%
4 76
 
0.3%
1 22
 
0.1%
7 18
 
0.1%
5 17
 
0.1%
6 10
 
< 0.1%
ValueCountFrequency (%)
-2 3078
 
12.8%
-1 4818
 
20.1%
0 12607
52.5%
1 22
 
0.1%
2 3109
 
13.0%
3 244
 
1.0%
4 76
 
0.3%
5 17
 
0.1%
6 10
 
< 0.1%
7 18
 
0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 18
 
0.1%
6 10
 
< 0.1%
5 17
 
0.1%
4 76
 
0.3%
3 244
 
1.0%
2 3109
 
13.0%
1 22
 
0.1%
0 12607
52.5%
-1 4818
 
20.1%

RETRASO_PAGO_ESTADO_9
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.023708333
Minimum-2
Maximum8
Zeros11810
Zeros (%)49.2%
Negative6784
Negative (%)28.3%
Memory size891.0 KiB
2023-04-01T01:17:04.261205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.121546
Coefficient of variation (CV)-47.30598
Kurtosis2.8618707
Mean-0.023708333
Median Absolute Deviation (MAD)1
Skewness0.74048891
Sum-569
Variance1.2578653
MonotonicityNot monotonic
2023-04-01T01:17:04.342487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11810
49.2%
-1 4547
 
18.9%
1 2956
 
12.3%
-2 2237
 
9.3%
2 2085
 
8.7%
3 258
 
1.1%
4 55
 
0.2%
5 21
 
0.1%
8 17
 
0.1%
7 7
 
< 0.1%
ValueCountFrequency (%)
-2 2237
 
9.3%
-1 4547
 
18.9%
0 11810
49.2%
1 2956
 
12.3%
2 2085
 
8.7%
3 258
 
1.1%
4 55
 
0.2%
5 21
 
0.1%
6 7
 
< 0.1%
7 7
 
< 0.1%
ValueCountFrequency (%)
8 17
 
0.1%
7 7
 
< 0.1%
6 7
 
< 0.1%
5 21
 
0.1%
4 55
 
0.2%
3 258
 
1.1%
2 2085
 
8.7%
1 2956
 
12.3%
0 11810
49.2%
-1 4547
 
18.9%

DEUDA_MES_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16967
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38585.38
Minimum-339603
Maximum699944
Zeros3264
Zeros (%)13.6%
Negative546
Negative (%)2.3%
Memory size891.0 KiB
2023-04-01T01:17:04.446487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11193.75
median16912.5
Q349010.5
95-th percentile160083.2
Maximum699944
Range1039547
Interquartile range (IQR)47816.75

Descriptive statistics

Standard deviation59064.025
Coefficient of variation (CV)1.5307359
Kurtosis10.657343
Mean38585.38
Median Absolute Deviation (MAD)16609.5
Skewness2.7614405
Sum9.2604911 × 108
Variance3.488559 × 109
MonotonicityNot monotonic
2023-04-01T01:17:04.559192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3264
 
13.6%
390 172
 
0.7%
780 67
 
0.3%
316 63
 
0.3%
150 57
 
0.2%
326 46
 
0.2%
396 38
 
0.2%
2400 29
 
0.1%
416 27
 
0.1%
-18 27
 
0.1%
Other values (16957) 20210
84.2%
ValueCountFrequency (%)
-339603 1
< 0.1%
-209051 1
< 0.1%
-150953 1
< 0.1%
-73895 1
< 0.1%
-57060 1
< 0.1%
-51443 1
< 0.1%
-51183 1
< 0.1%
-45734 1
< 0.1%
-39046 1
< 0.1%
-36156 1
< 0.1%
ValueCountFrequency (%)
699944 1
< 0.1%
568638 1
< 0.1%
527711 1
< 0.1%
527566 1
< 0.1%
514975 1
< 0.1%
513798 1
< 0.1%
511905 1
< 0.1%
501370 1
< 0.1%
496801 1
< 0.1%
489200 1
< 0.1%

DEUDA_MES_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17283
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40084.659
Minimum-37594
Maximum823540
Zeros2844
Zeros (%)11.8%
Negative521
Negative (%)2.2%
Memory size891.0 KiB
2023-04-01T01:17:04.675164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-37594
5-th percentile0
Q11700
median18068
Q350092
95-th percentile165077.1
Maximum823540
Range861134
Interquartile range (IQR)48392

Descriptive statistics

Standard deviation60254.188
Coefficient of variation (CV)1.5031733
Kurtosis11.006716
Mean40084.659
Median Absolute Deviation (MAD)17672
Skewness2.8041867
Sum9.620318 × 108
Variance3.6305672 × 109
MonotonicityNot monotonic
2023-04-01T01:17:04.815160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2844
 
11.8%
390 200
 
0.8%
780 78
 
0.3%
316 63
 
0.3%
326 50
 
0.2%
150 48
 
0.2%
396 40
 
0.2%
2400 36
 
0.1%
2500 28
 
0.1%
416 28
 
0.1%
Other values (17273) 20585
85.8%
ValueCountFrequency (%)
-37594 1
< 0.1%
-36156 1
< 0.1%
-28335 1
< 0.1%
-20753 1
< 0.1%
-20320 1
< 0.1%
-20254 1
< 0.1%
-20006 1
< 0.1%
-19205 1
< 0.1%
-15000 1
< 0.1%
-10810 1
< 0.1%
ValueCountFrequency (%)
823540 1
< 0.1%
587067 1
< 0.1%
551702 1
< 0.1%
547880 1
< 0.1%
516139 1
< 0.1%
508213 1
< 0.1%
503914 1
< 0.1%
500723 1
< 0.1%
489200 1
< 0.1%
483003 1
< 0.1%

DEUDA_MES_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17738
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42946.1
Minimum-34503
Maximum706864
Zeros2556
Zeros (%)10.7%
Negative553
Negative (%)2.3%
Memory size891.0 KiB
2023-04-01T01:17:04.960246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-34503
5-th percentile0
Q12215.75
median18989.5
Q354289
95-th percentile172617.95
Maximum706864
Range741367
Interquartile range (IQR)52073.25

Descriptive statistics

Standard deviation63618.015
Coefficient of variation (CV)1.4813456
Kurtosis10.265179
Mean42946.1
Median Absolute Deviation (MAD)18599.5
Skewness2.754728
Sum1.0307064 × 109
Variance4.0472518 × 109
MonotonicityNot monotonic
2023-04-01T01:17:05.081506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2556
 
10.7%
390 203
 
0.8%
780 83
 
0.3%
316 59
 
0.2%
326 48
 
0.2%
396 38
 
0.2%
2400 36
 
0.1%
150 29
 
0.1%
2500 28
 
0.1%
416 24
 
0.1%
Other values (17728) 20896
87.1%
ValueCountFrequency (%)
-34503 1
< 0.1%
-27490 1
< 0.1%
-24303 1
< 0.1%
-22108 1
< 0.1%
-20320 1
< 0.1%
-17250 1
< 0.1%
-15910 1
< 0.1%
-15588 1
< 0.1%
-15000 1
< 0.1%
-10938 1
< 0.1%
ValueCountFrequency (%)
706864 1
< 0.1%
616836 1
< 0.1%
572805 1
< 0.1%
569034 1
< 0.1%
563543 1
< 0.1%
542653 1
< 0.1%
541019 1
< 0.1%
530672 1
< 0.1%
518741 1
< 0.1%
516575 1
< 0.1%

DEUDA_MES_7
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18111
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46710.166
Minimum-157264
Maximum1664089
Zeros2287
Zeros (%)9.5%
Negative532
Negative (%)2.2%
Memory size891.0 KiB
2023-04-01T01:17:05.201473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12552
median20046
Q359911
95-th percentile186160.3
Maximum1664089
Range1821353
Interquartile range (IQR)57359

Descriptive statistics

Standard deviation68904.428
Coefficient of variation (CV)1.4751484
Kurtosis21.941257
Mean46710.166
Median Absolute Deviation (MAD)19663
Skewness3.1458341
Sum1.121044 × 109
Variance4.7478201 × 109
MonotonicityNot monotonic
2023-04-01T01:17:05.331736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2287
 
9.5%
390 220
 
0.9%
780 62
 
0.3%
316 52
 
0.2%
326 51
 
0.2%
396 42
 
0.2%
2400 36
 
0.1%
2500 32
 
0.1%
416 24
 
0.1%
-200 23
 
0.1%
Other values (18101) 21171
88.2%
ValueCountFrequency (%)
-157264 1
< 0.1%
-61506 1
< 0.1%
-34041 1
< 0.1%
-20320 1
< 0.1%
-17706 1
< 0.1%
-15910 1
< 0.1%
-15000 1
< 0.1%
-14998 1
< 0.1%
-11925 1
< 0.1%
-10951 1
< 0.1%
ValueCountFrequency (%)
1664089 1
< 0.1%
693131 1
< 0.1%
689643 1
< 0.1%
689627 1
< 0.1%
632041 1
< 0.1%
597415 1
< 0.1%
578971 1
< 0.1%
577957 1
< 0.1%
565550 1
< 0.1%
559712 1
< 0.1%

DEUDA_MES_8
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18370
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48808.869
Minimum-69777
Maximum743970
Zeros2035
Zeros (%)8.5%
Negative544
Negative (%)2.3%
Memory size891.0 KiB
2023-04-01T01:17:05.459740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12879.75
median20939.5
Q363358.25
95-th percentile193298.2
Maximum743970
Range813747
Interquartile range (IQR)60478.5

Descriptive statistics

Standard deviation70593.223
Coefficient of variation (CV)1.4463196
Kurtosis9.4099777
Mean48808.869
Median Absolute Deviation (MAD)20559.5
Skewness2.650003
Sum1.1714129 × 109
Variance4.9834031 × 109
MonotonicityNot monotonic
2023-04-01T01:17:05.571735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2035
 
8.5%
390 186
 
0.8%
326 61
 
0.3%
780 61
 
0.3%
316 59
 
0.2%
396 44
 
0.2%
2400 38
 
0.2%
2500 37
 
0.2%
-200 28
 
0.1%
416 23
 
0.1%
Other values (18360) 21428
89.3%
ValueCountFrequency (%)
-69777 1
< 0.1%
-67526 1
< 0.1%
-30000 1
< 0.1%
-24704 1
< 0.1%
-24702 1
< 0.1%
-22960 1
< 0.1%
-18618 1
< 0.1%
-18088 1
< 0.1%
-17810 1
< 0.1%
-17710 1
< 0.1%
ValueCountFrequency (%)
743970 1
< 0.1%
671563 1
< 0.1%
646770 1
< 0.1%
624475 1
< 0.1%
597793 1
< 0.1%
586825 1
< 0.1%
572834 1
< 0.1%
572677 1
< 0.1%
569577 1
< 0.1%
562316 1
< 0.1%

DEUDA_MES_9
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18677
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50861.993
Minimum-165580
Maximum746814
Zeros1638
Zeros (%)6.8%
Negative467
Negative (%)1.9%
Memory size891.0 KiB
2023-04-01T01:17:05.709096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13456.5
median21989.5
Q366452.75
95-th percentile200823.35
Maximum746814
Range912394
Interquartile range (IQR)62996.25

Descriptive statistics

Standard deviation73227.356
Coefficient of variation (CV)1.4397264
Kurtosis9.1911661
Mean50861.993
Median Absolute Deviation (MAD)21475
Skewness2.6314157
Sum1.2206878 × 109
Variance5.3622457 × 109
MonotonicityNot monotonic
2023-04-01T01:17:05.825772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1638
 
6.8%
390 192
 
0.8%
780 68
 
0.3%
326 58
 
0.2%
316 52
 
0.2%
2500 42
 
0.2%
396 40
 
0.2%
2400 36
 
0.1%
416 23
 
0.1%
1261 21
 
0.1%
Other values (18667) 21830
91.0%
ValueCountFrequency (%)
-165580 1
< 0.1%
-14386 1
< 0.1%
-11545 1
< 0.1%
-9802 1
< 0.1%
-9095 1
< 0.1%
-8187 1
< 0.1%
-7438 1
< 0.1%
-6676 1
< 0.1%
-6028 1
< 0.1%
-6027 1
< 0.1%
ValueCountFrequency (%)
746814 1
< 0.1%
653062 1
< 0.1%
630458 1
< 0.1%
626648 1
< 0.1%
613860 1
< 0.1%
610723 1
< 0.1%
608594 1
< 0.1%
588000 1
< 0.1%
581775 1
< 0.1%
580928 1
< 0.1%

PAGO_MES_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6016
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5226.2065
Minimum0
Maximum528666
Zeros5786
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size891.0 KiB
2023-04-01T01:17:05.946992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q199
median1500
Q34000
95-th percentile17190.65
Maximum528666
Range528666
Interquartile range (IQR)3901

Descriptive statistics

Standard deviation17725.863
Coefficient of variation (CV)3.3917265
Kurtosis146.98447
Mean5226.2065
Median Absolute Deviation (MAD)1500
Skewness10.087805
Sum1.2542896 × 108
Variance3.1420622 × 108
MonotonicityNot monotonic
2023-04-01T01:17:06.104218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5786
24.1%
1000 1030
 
4.3%
2000 1023
 
4.3%
3000 729
 
3.0%
5000 650
 
2.7%
1500 362
 
1.5%
4000 332
 
1.4%
10000 290
 
1.2%
500 194
 
0.8%
6000 170
 
0.7%
Other values (6006) 13434
56.0%
ValueCountFrequency (%)
0 5786
24.1%
1 14
 
0.1%
2 8
 
< 0.1%
3 10
 
< 0.1%
4 10
 
< 0.1%
5 6
 
< 0.1%
6 4
 
< 0.1%
7 5
 
< 0.1%
8 5
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
528666 1
< 0.1%
422000 1
< 0.1%
403500 1
< 0.1%
377000 1
< 0.1%
372495 1
< 0.1%
351282 1
< 0.1%
345293 1
< 0.1%
290000 1
< 0.1%
280000 1
< 0.1%
279706 1
< 0.1%

PAGO_MES_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5962
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4723.4396
Minimum0
Maximum426529
Zeros5384
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size891.0 KiB
2023-04-01T01:17:06.228217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1246
median1500
Q34000
95-th percentile15963.75
Maximum426529
Range426529
Interquartile range (IQR)3754

Descriptive statistics

Standard deviation14835.961
Coefficient of variation (CV)3.1409232
Kurtosis189.31371
Mean4723.4396
Median Absolute Deviation (MAD)1500
Skewness11.226338
Sum1.1336255 × 108
Variance2.2010574 × 108
MonotonicityNot monotonic
2023-04-01T01:17:06.356250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5384
 
22.4%
1000 1086
 
4.5%
2000 1033
 
4.3%
3000 781
 
3.3%
5000 644
 
2.7%
1500 352
 
1.5%
4000 327
 
1.4%
10000 272
 
1.1%
500 198
 
0.8%
6000 195
 
0.8%
Other values (5952) 13728
57.2%
ValueCountFrequency (%)
0 5384
22.4%
1 14
 
0.1%
2 9
 
< 0.1%
3 6
 
< 0.1%
4 9
 
< 0.1%
5 5
 
< 0.1%
6 6
 
< 0.1%
7 6
 
< 0.1%
8 4
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
426529 1
< 0.1%
417990 1
< 0.1%
388071 1
< 0.1%
379267 1
< 0.1%
330982 1
< 0.1%
317077 1
< 0.1%
302823 1
< 0.1%
300000 1
< 0.1%
287982 1
< 0.1%
284069 1
< 0.1%

PAGO_MES_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6036
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4740.7755
Minimum0
Maximum432130
Zeros5182
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size891.0 KiB
2023-04-01T01:17:06.476252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1290
median1500
Q34000.25
95-th percentile16000
Maximum432130
Range432130
Interquartile range (IQR)3710.25

Descriptive statistics

Standard deviation14429.99
Coefficient of variation (CV)3.0438036
Kurtosis153.34444
Mean4740.7755
Median Absolute Deviation (MAD)1500
Skewness10.227704
Sum1.1377861 × 108
Variance2.082246 × 108
MonotonicityNot monotonic
2023-04-01T01:17:06.592251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5182
 
21.6%
1000 1109
 
4.6%
2000 959
 
4.0%
3000 718
 
3.0%
5000 633
 
2.6%
1500 349
 
1.5%
4000 331
 
1.4%
10000 264
 
1.1%
2500 210
 
0.9%
500 209
 
0.9%
Other values (6026) 14036
58.5%
ValueCountFrequency (%)
0 5182
21.6%
1 18
 
0.1%
2 17
 
0.1%
3 11
 
< 0.1%
4 16
 
0.1%
5 10
 
< 0.1%
6 11
 
< 0.1%
7 10
 
< 0.1%
8 6
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
432130 1
< 0.1%
330982 1
< 0.1%
320008 1
< 0.1%
313094 1
< 0.1%
292962 1
< 0.1%
281225 1
< 0.1%
280695 1
< 0.1%
265852 1
< 0.1%
256662 1
< 0.1%
253009 1
< 0.1%

PAGO_MES_7
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6476
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5099.7523
Minimum0
Maximum508229
Zeros4801
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size891.0 KiB
2023-04-01T01:17:06.712251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1777
Q34402
95-th percentile17385.5
Maximum508229
Range508229
Interquartile range (IQR)4012

Descriptive statistics

Standard deviation16038.155
Coefficient of variation (CV)3.1448891
Kurtosis206.44852
Mean5099.7523
Median Absolute Deviation (MAD)1765
Skewness11.805573
Sum1.2239406 × 108
Variance2.5722243 × 108
MonotonicityNot monotonic
2023-04-01T01:17:06.832249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4801
 
20.0%
2000 1009
 
4.2%
1000 879
 
3.7%
3000 711
 
3.0%
5000 581
 
2.4%
1500 402
 
1.7%
4000 314
 
1.3%
10000 249
 
1.0%
2500 196
 
0.8%
1200 196
 
0.8%
Other values (6466) 14662
61.1%
ValueCountFrequency (%)
0 4801
20.0%
1 9
 
< 0.1%
2 16
 
0.1%
3 8
 
< 0.1%
4 10
 
< 0.1%
5 11
 
< 0.1%
6 10
 
< 0.1%
7 10
 
< 0.1%
8 8
 
< 0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
508229 1
< 0.1%
400972 1
< 0.1%
397092 1
< 0.1%
380478 1
< 0.1%
371718 1
< 0.1%
349395 1
< 0.1%
344261 1
< 0.1%
338394 1
< 0.1%
332809 1
< 0.1%
326974 1
< 0.1%

PAGO_MES_8
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6829
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5906.9451
Minimum0
Maximum1684259
Zeros4284
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size891.0 KiB
2023-04-01T01:17:06.957432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1850
median2007.5
Q35000
95-th percentile19000.05
Maximum1684259
Range1684259
Interquartile range (IQR)4150

Descriptive statistics

Standard deviation22312.903
Coefficient of variation (CV)3.7774014
Kurtosis1786.3198
Mean5906.9451
Median Absolute Deviation (MAD)1992.5
Skewness30.503173
Sum1.4176668 × 108
Variance4.9786562 × 108
MonotonicityNot monotonic
2023-04-01T01:17:07.081452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4284
 
17.8%
2000 1032
 
4.3%
3000 714
 
3.0%
5000 570
 
2.4%
1000 459
 
1.9%
1500 408
 
1.7%
4000 331
 
1.4%
10000 244
 
1.0%
6000 232
 
1.0%
2500 204
 
0.9%
Other values (6819) 15522
64.7%
ValueCountFrequency (%)
0 4284
17.8%
1 7
 
< 0.1%
2 12
 
0.1%
3 12
 
0.1%
4 9
 
< 0.1%
5 16
 
0.1%
6 6
 
< 0.1%
7 10
 
< 0.1%
8 7
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
1684259 1
< 0.1%
1227082 1
< 0.1%
580464 1
< 0.1%
415552 1
< 0.1%
401003 1
< 0.1%
388126 1
< 0.1%
385228 1
< 0.1%
384986 1
< 0.1%
368199 1
< 0.1%
361560 1
< 0.1%

PAGO_MES_9
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6892
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5654.719
Minimum0
Maximum505000
Zeros4225
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size891.0 KiB
2023-04-01T01:17:07.205452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1974
median2100
Q35004
95-th percentile18261
Maximum505000
Range505000
Interquartile range (IQR)4030

Descriptive statistics

Standard deviation16149.946
Coefficient of variation (CV)2.8560121
Kurtosis208.91859
Mean5654.719
Median Absolute Deviation (MAD)1926
Skewness11.710893
Sum1.3571326 × 108
Variance2.6082075 × 108
MonotonicityNot monotonic
2023-04-01T01:17:07.329453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4225
 
17.6%
2000 1102
 
4.6%
3000 718
 
3.0%
5000 562
 
2.3%
1500 400
 
1.7%
4000 342
 
1.4%
10000 328
 
1.4%
1000 298
 
1.2%
6000 236
 
1.0%
2500 236
 
1.0%
Other values (6882) 15553
64.8%
ValueCountFrequency (%)
0 4225
17.6%
1 8
 
< 0.1%
2 13
 
0.1%
3 13
 
0.1%
4 12
 
0.1%
5 8
 
< 0.1%
6 11
 
< 0.1%
7 7
 
< 0.1%
8 5
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
505000 1
< 0.1%
493358 1
< 0.1%
423903 1
< 0.1%
405016 1
< 0.1%
368199 1
< 0.1%
323014 1
< 0.1%
304815 1
< 0.1%
302000 1
< 0.1%
300000 1
< 0.1%
298887 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size891.0 KiB
0
18715 
1
5285 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18715
78.0%
1 5285
 
22.0%

Length

2023-04-01T01:17:07.629452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T01:17:07.713452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18715
78.0%
1 5285
 
22.0%

Most occurring characters

ValueCountFrequency (%)
0 18715
78.0%
1 5285
 
22.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18715
78.0%
1 5285
 
22.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18715
78.0%
1 5285
 
22.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18715
78.0%
1 5285
 
22.0%

Interactions

2023-04-01T01:16:59.011460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:15.743398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:18.220077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:21.178186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:23.564578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:26.019601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:28.194538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:30.257115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:32.557645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:34.567214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:36.870179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:38.908282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:41.193516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:43.612325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:45.922276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:48.154149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:50.266110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:52.359817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:54.510504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:56.673954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:59.119464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:15.875669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:18.336046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:21.310194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:23.672629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:26.156578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:28.302537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:30.364045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:32.673656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:34.687214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:36.979428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:39.016245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:41.322271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:43.752321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:46.034241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:48.262147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:50.378937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:52.471817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:54.614504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:56.793910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:59.219461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:15.988776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:18.438085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:21.528863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:23.800590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:26.321289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:28.398576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:30.460048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:32.777739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:34.807212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:37.081506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:39.124477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:41.460527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:43.860322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:46.149452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:48.358150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:50.479689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:52.575821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:54.722518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:56.911128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:59.315462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:16.112923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:18.542848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:21.651422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:23.984978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:26.428084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:28.498572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:30.560059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:32.873740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:34.940428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:37.181515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:39.225167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:41.576561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:43.966753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:46.249479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:48.458150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:50.575690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:52.671818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:54.867741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:57.015130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:59.411461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:16.220963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:18.654808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:21.756069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:24.085279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:26.536049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:28.590537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:30.656008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:32.966884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:35.042655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:37.278232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:39.335427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:41.700567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-04-01T01:16:47.383111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:49.594633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:51.745517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:53.938504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:56.055458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:58.376309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:17:00.613199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:17.600827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:20.380133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:22.995002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:25.293826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:27.713445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:29.713844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:32.004624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:34.085711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:36.182220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:38.389909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:40.602370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:43.084971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:45.374282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:47.667106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:49.698631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:51.846191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:54.034506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:56.161164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:58.476313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:17:00.709194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:17.708833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:20.572102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:23.122999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:25.391996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:27.805447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:29.829843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:32.107907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:34.177711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:36.278179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:38.483345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:40.716131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:43.185636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:45.478100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:47.763105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:49.822633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:51.959862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:54.126503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:56.265912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:58.572311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:17:00.805229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:17.812824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:20.736391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:23.235036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:25.512037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:27.898544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:29.950956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:32.205678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:34.269712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:36.374179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:38.576038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:40.820366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:43.289635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:45.582783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:47.859072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:49.943710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:52.059819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:54.218505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:56.365911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:58.672312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:17:00.910486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:17.922043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:20.888718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:23.339036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:25.798407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:27.994576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:30.055664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:32.309680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:34.367009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:36.662217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:38.676038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:40.942118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:43.391673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:45.694783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:47.956179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:50.048666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:52.155854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:54.310505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:56.465943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:58.776312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:17:01.034745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:18.079355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:21.053882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:23.460442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:25.911572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:28.098573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:30.159664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:32.425645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:34.471214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:36.770213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:38.798115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:41.067327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:43.500315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:45.812326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:48.061468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:50.156628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:52.263885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:54.414504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:56.573947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T01:16:58.890785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-01T01:17:07.805417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Cupo_TCAGERETRASO_PAGO_ESTADO_4RETRASO_PAGO_ESTADO_5RETRASO_PAGO_ESTADO_6RETRASO_PAGO_ESTADO_7RETRASO_PAGO_ESTADO_8RETRASO_PAGO_ESTADO_9DEUDA_MES_4DEUDA_MES_5DEUDA_MES_6DEUDA_MES_7DEUDA_MES_8DEUDA_MES_9PAGO_MES_4PAGO_MES_5PAGO_MES_6PAGO_MES_7PAGO_MES_8PAGO_MES_9SEXEDUCATIONMARRIAGEdefault payment next month
Cupo_TC1.0000.187-0.268-0.287-0.312-0.338-0.351-0.3050.0850.0780.0690.0580.0440.0500.3160.2930.2790.2840.2810.2710.0870.1140.0680.164
AGE0.1871.000-0.078-0.082-0.080-0.085-0.085-0.0660.0020.001-0.0010.0030.0020.0030.0410.0390.0380.0360.0460.0350.0910.1140.2860.044
RETRASO_PAGO_ESTADO_4-0.268-0.0781.0000.8200.7340.6740.6380.4620.6290.6650.6060.5620.5250.4920.2000.1460.2850.2400.2040.1800.0450.0700.0290.247
RETRASO_PAGO_ESTADO_5-0.287-0.0820.8201.0000.8240.7220.6790.4850.5800.6190.6500.5900.5410.5050.1750.1920.1090.2650.2260.1780.0540.0730.0320.272
RETRASO_PAGO_ESTADO_6-0.312-0.0800.7340.8241.0000.8030.7170.5160.5360.5640.5970.6210.5610.5170.1460.1680.1470.0800.2500.1900.0640.0780.0350.283
RETRASO_PAGO_ESTADO_7-0.338-0.0850.6740.7220.8031.0000.8010.5450.4870.5100.5360.5590.5910.5280.1010.1310.1220.1120.0440.2190.0690.0860.0300.298
RETRASO_PAGO_ESTADO_8-0.351-0.0850.6380.6790.7170.8011.0000.6250.4610.4790.4990.5190.5520.5730.0850.1050.0960.0940.0880.0260.0690.0890.0320.342
RETRASO_PAGO_ESTADO_9-0.305-0.0660.4620.4850.5160.5450.6251.0000.2880.2960.3050.3100.3260.311-0.045-0.026-0.037-0.050-0.061-0.0980.0620.0820.0340.424
DEUDA_MES_40.0850.0020.6290.5800.5360.4870.4610.2881.0000.9030.8490.8040.7670.7370.5310.6720.5700.5260.4910.4610.0240.0170.0170.007
DEUDA_MES_50.0780.0010.6650.6190.5640.5100.4790.2960.9031.0000.9050.8510.8050.7720.5100.5320.6490.5570.5230.4880.0210.0230.0180.023
DEUDA_MES_60.069-0.0010.6060.6500.5970.5360.4990.3050.8490.9051.0000.9070.8510.8110.4820.5120.5070.6400.5610.5180.0180.0280.0150.018
DEUDA_MES_70.0580.0030.5620.5900.6210.5590.5190.3100.8040.8510.9071.0000.9080.8590.4600.4830.4890.5010.6410.5540.0180.0470.0150.000
DEUDA_MES_80.0440.0020.5250.5410.5610.5910.5520.3260.7670.8050.8510.9081.0000.9110.4310.4550.4620.4750.5000.6390.0470.0660.0250.035
DEUDA_MES_90.0500.0030.4920.5050.5170.5280.5730.3110.7370.7720.8110.8590.9111.0000.4120.4330.4420.4480.4750.5050.0410.0660.0220.021
PAGO_MES_40.3160.0410.2000.1750.1460.1010.085-0.0450.5310.5100.4820.4600.4310.4121.0000.5510.5470.5080.4950.4570.0090.0190.0000.032
PAGO_MES_50.2930.0390.1460.1920.1680.1310.105-0.0260.6720.5320.5120.4830.4550.4330.5511.0000.5360.5380.5030.4730.0140.0110.0110.032
PAGO_MES_60.2790.0380.2850.1090.1470.1220.096-0.0370.5700.6490.5070.4890.4620.4420.5470.5361.0000.5180.5230.4890.0170.0020.0000.035
PAGO_MES_70.2840.0360.2400.2650.0800.1120.094-0.0500.5260.5570.6400.5010.4750.4480.5080.5380.5181.0000.5220.5230.0120.0230.0000.036
PAGO_MES_80.2810.0460.2040.2260.2500.0440.088-0.0610.4910.5230.5610.6410.5000.4750.4950.5030.5230.5221.0000.5140.0000.0000.0000.009
PAGO_MES_90.2710.0350.1800.1780.1900.2190.026-0.0980.4610.4880.5180.5540.6390.5050.4570.4730.4890.5230.5141.0000.0000.0070.0000.039
SEX0.0870.0910.0450.0540.0640.0690.0690.0620.0240.0210.0180.0180.0470.0410.0090.0140.0170.0120.0000.0001.0000.0270.0270.041
EDUCATION0.1140.1140.0700.0730.0780.0860.0890.0820.0170.0230.0280.0470.0660.0660.0190.0110.0020.0230.0000.0070.0271.0000.1150.072
MARRIAGE0.0680.2860.0290.0320.0350.0300.0320.0340.0170.0180.0150.0150.0250.0220.0000.0110.0000.0000.0000.0000.0270.1151.0000.029
default payment next month0.1640.0440.2470.2720.2830.2980.3420.4240.0070.0230.0180.0000.0350.0210.0320.0320.0350.0360.0090.0390.0410.0720.0291.000

Missing values

2023-04-01T01:17:01.235844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-01T01:17:01.592215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Cupo_TCSEXEDUCATIONMARRIAGEAGERETRASO_PAGO_ESTADO_4RETRASO_PAGO_ESTADO_5RETRASO_PAGO_ESTADO_6RETRASO_PAGO_ESTADO_7RETRASO_PAGO_ESTADO_8RETRASO_PAGO_ESTADO_9DEUDA_MES_4DEUDA_MES_5DEUDA_MES_6DEUDA_MES_7DEUDA_MES_8DEUDA_MES_9PAGO_MES_4PAGO_MES_5PAGO_MES_6PAGO_MES_7PAGO_MES_8PAGO_MES_9default payment next month
CLIENT_ID
29c55f5f-0da7-43ec-ba1b-b60592689825270000FemeninouniversidadCasado2800000092618940622635163081332987003222495000400050001200013000100000
f818136a-138f-4578-a0bb-41605b67e0e580000Femeninocolegiosoltero_a52-10000074321830630470360603950842635074323661200174016230
bef609a9-f9c5-4870-be7f-cb20e70f7e92210000FemeninouniversidadCasado360000001316291479931481901444151429961432254700454459006000508264830
2ba3a218-4792-4672-82c7-4b0953101d41230000FemeninocolegioCasado44-1-1-1-1-1-1174251512162224987116325479817007174251512162224987116320
723e61b4-d151-4368-b129-468417fd930c130000FemeninoPosgradoCasado360000009239490556946231039421070701296894000350040005000440050000
f689cc58-4331-40ba-ae4b-c43e649cb9e8470000FemeninouniversidadCasado350000005736859563615005930757920648751600300022003016300025050
5366b666-0978-4843-9ad0-a23993b94edd800000MasculinouniversidadCasado53-100-1-1-193391259040814645355552763963482936624713164657111450
92825b72-b828-419b-b2b1-0c479176955820000Masculinouniversidadsoltero_a30000000152981353410717198122022119149399200030001000120113920
bd9b31c5-e0d6-4555-afab-ed6753d6afb840000Masculinocolegiosoltero_a437777222400240024002400240024000000001
2c304969-82e6-4981-b2e2-af1f1977b47f10000Masculinouniversidadsoltero_a237777222400240024002400240024000000001
Cupo_TCSEXEDUCATIONMARRIAGEAGERETRASO_PAGO_ESTADO_4RETRASO_PAGO_ESTADO_5RETRASO_PAGO_ESTADO_6RETRASO_PAGO_ESTADO_7RETRASO_PAGO_ESTADO_8RETRASO_PAGO_ESTADO_9DEUDA_MES_4DEUDA_MES_5DEUDA_MES_6DEUDA_MES_7DEUDA_MES_8DEUDA_MES_9PAGO_MES_4PAGO_MES_5PAGO_MES_6PAGO_MES_7PAGO_MES_8PAGO_MES_9default payment next month
CLIENT_ID
6089e156-cb40-4811-9b13-236fad90b2a7160000FemeninoPosgradoCasado2800023107807801170426945290000001
f733ac93-7080-4f65-a266-4a6d27f22de360000MasculinocolegioCasado380022222814725212244042508632232330442600350015000400001
4fcb4e53-1415-4975-8361-9432d6523ec870000Femeninocolegiosoltero_a2600000012302134081157810030836171392000100020002000200015001
484bec12-e125-464a-b7a8-09487d4122e2320000MasculinouniversidadCasado3900-1-1009690025880013191618901803102434531319380013040013284618901800
ed7853ab-7987-43b2-8b83-8228832c57b3380000FemeninoPosgradoCasado4300000086661840871013231025511146271218685000500050005000500060000
ba2501de-d42f-428d-8dff-0165f0e30911280000Masculinouniversidadsoltero_a45000-1009642511145413624919149326031170678300050005002500320814170000
ce703c87-fa0d-45e6-bc88-8c1db81a9b35180000FemeninouniversidadCasado4600002130680231232657427555307113591601300011237504130017110
43e0e2f5-6a83-4abe-a18f-622fc311f33a180000MasculinouniversidadCasado35000021033370333703157031293320820002200140000
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3572ab87-7de9-4a74-9e06-3db4990fc796260000Femeninouniversidadsoltero_a40-2-2-2-2-1-1000010851022300000010850

Duplicate rows

Most frequently occurring

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020000Masculinouniversidadsoltero_a2444442216501650165016501650165000000012
150000FemeninoPosgradosoltero_a23-2-2-2-2-2100000000000002
250000Masculinouniversidadsoltero_a26-2-2-2-2-2100000000000002
380000Femeninouniversidadsoltero_a25-2-2-2-2-2-200000000000002
490000FemeninoPosgradosoltero_a31-2-2-2-2-2100000000000002
5100000FemeninouniversidadCasado49-2-2-2-2-2100000000000002
6140000MasculinoPosgradosoltero_a29-2-2-2-2-2100000000000002
7150000FemeninoPosgradoCasado38-2-2-2-2-2100000000000012
8150000FemeninoPosgradosoltero_a28-2-2-2-2-2100000000000002
9160000Masculinouniversidadsoltero_a28-2-2-2-2-2-200000000000002